{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BV7EHK5JO74LYI65GARSC5YZUI","short_pith_number":"pith:BV7EHK5J","schema_version":"1.0","canonical_sha256":"0d7e43aba977f8bc23dd3023217719a21c34cab320fa34043c65afb165a20d7a","source":{"kind":"arxiv","id":"2501.06386","version":1},"attestation_state":"computed","paper":{"title":"Using Pre-trained LLMs for Multivariate Time Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Kari Torkkola, Malcolm L. Wolff, Michael W. Mahoney, Shenghao Yang","submitted_at":"2025-01-10T23:30:23Z","abstract_excerpt":"Pre-trained Large Language Models (LLMs) encapsulate large amounts of knowledge and take enormous amounts of compute to train. We make use of this resource, together with the observation that LLMs are able to transfer knowledge and performance from one domain or even modality to another seemingly-unrelated area, to help with multivariate demand time series forecasting. Attention in transformer-based methods requires something worth attending to -- more than just samples of a time-series. We explore different methods to map multivariate input time series into the LLM token embedding space. In p"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.06386","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-10T23:30:23Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"ce8bb99cfaeeb06e93d24d836577873fa6962584c312f6fcefbfacebee57c6b5","abstract_canon_sha256":"1de01d6c0c890c8a1fc33d071e99eee516c13750a6213d5258d89811bb805fed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:00:27.993013Z","signature_b64":"OrQ7IZKcodGI8RUhVuAbIztRqVqgOE6qmYbu8iWwlB4YW5FPLgEJWx/6ksK3AorUCxorIoAIU2ahpL01YorsBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d7e43aba977f8bc23dd3023217719a21c34cab320fa34043c65afb165a20d7a","last_reissued_at":"2026-07-05T10:00:27.992524Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:00:27.992524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using Pre-trained LLMs for Multivariate Time Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Kari Torkkola, Malcolm L. Wolff, Michael W. Mahoney, Shenghao Yang","submitted_at":"2025-01-10T23:30:23Z","abstract_excerpt":"Pre-trained Large Language Models (LLMs) encapsulate large amounts of knowledge and take enormous amounts of compute to train. We make use of this resource, together with the observation that LLMs are able to transfer knowledge and performance from one domain or even modality to another seemingly-unrelated area, to help with multivariate demand time series forecasting. Attention in transformer-based methods requires something worth attending to -- more than just samples of a time-series. We explore different methods to map multivariate input time series into the LLM token embedding space. In p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.06386","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.06386/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.06386","created_at":"2026-07-05T10:00:27.992582+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.06386v1","created_at":"2026-07-05T10:00:27.992582+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.06386","created_at":"2026-07-05T10:00:27.992582+00:00"},{"alias_kind":"pith_short_12","alias_value":"BV7EHK5JO74L","created_at":"2026-07-05T10:00:27.992582+00:00"},{"alias_kind":"pith_short_16","alias_value":"BV7EHK5JO74LYI65","created_at":"2026-07-05T10:00:27.992582+00:00"},{"alias_kind":"pith_short_8","alias_value":"BV7EHK5J","created_at":"2026-07-05T10:00:27.992582+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.12120","citing_title":"Forecasting Commencing Enrolments Under Data Sparsity: A Zero-Shot Time Series Foundation Models Framework for Higher Education Planning","ref_index":65,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BV7EHK5JO74LYI65GARSC5YZUI","json":"https://pith.science/pith/BV7EHK5JO74LYI65GARSC5YZUI.json","graph_json":"https://pith.science/api/pith-number/BV7EHK5JO74LYI65GARSC5YZUI/graph.json","events_json":"https://pith.science/api/pith-number/BV7EHK5JO74LYI65GARSC5YZUI/events.json","paper":"https://pith.science/paper/BV7EHK5J"},"agent_actions":{"view_html":"https://pith.science/pith/BV7EHK5JO74LYI65GARSC5YZUI","download_json":"https://pith.science/pith/BV7EHK5JO74LYI65GARSC5YZUI.json","view_paper":"https://pith.science/paper/BV7EHK5J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.06386&json=true","fetch_graph":"https://pith.science/api/pith-number/BV7EHK5JO74LYI65GARSC5YZUI/graph.json","fetch_events":"https://pith.science/api/pith-number/BV7EHK5JO74LYI65GARSC5YZUI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BV7EHK5JO74LYI65GARSC5YZUI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BV7EHK5JO74LYI65GARSC5YZUI/action/storage_attestation","attest_author":"https://pith.science/pith/BV7EHK5JO74LYI65GARSC5YZUI/action/author_attestation","sign_citation":"https://pith.science/pith/BV7EHK5JO74LYI65GARSC5YZUI/action/citation_signature","submit_replication":"https://pith.science/pith/BV7EHK5JO74LYI65GARSC5YZUI/action/replication_record"}},"created_at":"2026-07-05T10:00:27.992582+00:00","updated_at":"2026-07-05T10:00:27.992582+00:00"}